System and method for modeling three dimensional objects from a single image

ABSTRACT

A system and method for automatically modeling a three dimensional object, such as a face, from a single image. The system and method according to the invention can construct one or more three dimensional (3D) face models using a single image. Although use of a frontal image simplifies the processing, the system and method according to the invention can also generate a 3D model from a non-frontal image (e.g., an image where the object to be modeled has an out of plane rotation, such a face that is turned to one side to some extent). The system is fully automatic. It is fast compared to the other modeling systems, Furthermore, it is very robust. It can be used to construct personalized models for games, on-line chat, and so on. It can also be used as a tool to generate a database of faces with various poses which are needed to train most face recognition systems.

The above-identified application is a continuation of a priorapplication entitled “System and Method for Modeling Three DimensionalObjects from a Single Image” which was assigned U.S. Ser. No.10/637,223, and was filed on Aug. 8, 2003.

BACKGROUND

1. Technical Field

This invention is directed toward a fully automatic system and methodfor modeling objects from an image. More specifically, the invention isdirected toward a fully automatic system and method for modelingthree-dimensional objects, such as faces, from a single image.

2. Background Art

There has been much work on face modeling from images. One techniquewhich has been used in many commercial systems employs two orthogonalviews—one frontal view and one side view in order to create a facemodel. This type of system requires the user to manually specify theface features on the two images in order to model the face. There are,however, some of these face-modeling systems employing more than oneinput image that have attempted to employ some feature finding methodsto reduce the amount of manual work necessary to create the resultingmodel.

Another type of face modeling system creates face models from a videosequence. Since this type of system has available images of multipleviews of the face to be modeled, it can potentially compute the correctdepth and can generate a texture image for an entire face. However, thistype of system requires the user to have a video camera. In addition,this type of system requires some amount of user input to renderaccurate models and make it robust.

Another approach to generating face models from a single image,described by V. Blanz and T. Vetter [1], requires the use of both ageometry database and an image database to generate three-dimensional(3D) models. However, this approach can only model the people whose skintypes are covered by the database. In this approach, the database usedmainly consisted of Caucasian people. Therefore, it is difficult tomodel people of other races. It would require an extremely large imagedatabase to cover people of all races. Another problem with thismodeling approach is that the images in the database contain thelighting conditions when those images were taken. Given a new image, itslighting condition is in general different from the lighting conditionin the database. The approach described by Blanz et al. employs a linearmethod to adjust the lighting, but lighting condition changes cannot bemodeled very well by a linear technique. Therefore, the system hasdifficulties in handling arbitrary lighting conditions. In addition, itrequires manual initialization to provide the location of the face, itspose, and face features. Hence, the system is not fully automatic.Finally, this system is computationally expensive and not very robustbecause it has a large amount of unknowns, must perform a large numberof image operations, and a large percentage of the equations used arehighly nonlinear.

What is needed is a system that can create a 3D model of a face, orsimilar object, using a single image, that does not require userinteraction, is fast and computationally efficient, can model people ofany skin types in various lighting conditions, and is robust.

SUMMARY

The invention is directed toward a system and method for automaticallymodeling a three dimensional object, such as a face, from a singleimage. The system and method according to the invention can constructone or more three dimensional (3D) face models using a single image.Although use of a frontal image simplifies the processing, the systemand method according to the invention can also generate a 3D model froma non-frontal image (e.g., an image where the object to be modeled hasan out of plane rotation, such a face that is turned to one side to someextent).

In general, a single image of an object is input into the system andmethod according to the invention. An object detector is used to findthe object to be modeled in the image. Then the features of the objectare determined. If the feature determination process action shows thatthe object is rotated out of the plane of the image, the amount ofrotation is determined. This rotation information can be used to rotatea generic model of an object to match the pose of the object in theinput image. The features of the object to be modeled are then used toalign the feature points of the object in the image to a generic modelof the object. Once the generic model is so tailored to match the objectin the image, the texture of the originally input image can be applied.The tailored model can then be used for various applications.

For example, one embodiment of the invention is directed toward a facemodeling system and method. In this embodiment, a single image of a faceis input. A face detector is used to find the face to be modeled in theimage. Then the features of the object are determined. If the featuredetermination process shows that the face is rotated out of the plane ofthe image, the amount of out-of-plane rotation of the face is determinedThe out-of-plane rotation information can be used to align a genericneutral face model with the pose of the face in the image. The featuresare then used to align the feature points of the face in the image to ageneric face model. In one embodiment of the invention, a generic facemodel of a neutral face is employed as is discussed below. Once thegeneric face model is tailored to match the face in the image, thetexture of the original image can be applied to the tailored genericface model. The tailored model can then be used for variousapplications, such as animation, gaming, and preparing a database forface recognition training. Although this invention is described relativeto a face model, it should be noted that many other types of objectscould be modeled. For example, it is possible to create a generic modelof the human body as was done for the face using the same techniques ofthe process described above with a different generic model.

As to the specifics of the generic neutral face model discussed above,in one embodiment of the invention a face is represented as a linearcombination of a neutral face and some number of face metrics. In anillustrative embodiment, a face metric can correspond to a vector thatlinearly deforms aspects of a generic neutral face model in a certainmanner, such as to make the head wider, make the nose bigger, and so on.The face geometry is denoted by a vector S=(v₁ ^(T), . . . , v_(u)^(T))^(T) where v_(i)=(X_(i),Y_(i),Z_(i))^(T) (i=1, . . . , n) are thevertices, and a metric by a vector M=(δv_(i), . . . , δv_(u) ^(T))^(T),where δv_(i)=(δX_(i),δY_(i),δZ_(i))^(T). Given a neutral face S^(o)=(v₁^(0T), . . . , v_(n) ^(0T))^(T), and a set of m metrics M^(j)=(δv_(i)^(jT), . . . , δv_(n) ^(jT))^(T), the linear space of face geometriesspanned by these metrics is$S = {{S^{0} + {\sum\limits_{j = 1}^{m}{c_{j}M^{j}\quad{subject}\quad{to}\quad c_{j}}}} \in \left\lbrack {l_{j},u_{j}} \right\rbrack}$where the S is the new face model the c_(j)'s are the metriccoefficients (e.g., scalars to scale the metrics) which will bedifferent for different faces and I_(j) and u_(j) are the valid range ofc_(j). The neutral face and all of the associated metrics are typicallydesigned by an artist. It is only necessary to create the neutral facerepresentation once. In one embodiment of the invention, the neutralface representation contains 194 vertices and 360 triangles. There are65 metrics in this embodiment. It should be noted, however, that theneutral face could also be represented with less or more vertices andtriangles.

The system can be fully automatic. It is fast compared to the othermodeling systems (e.g., the system and method according to the inventionhas half the unknowns of the previously discussed Blanz approach to facemodeling). Furthermore, it is very robust. It can be used to constructpersonalized models for games, animation, on-line chat, and so on. Itcan also be used as a tool to generate a database of faces with variousposes which are needed to train most face recognition systems.

It is noted that in the remainder of this specification. the descriptionrefers to various individual publications identified by a numericdesignator contained within a pair of brackets. For example, such areference may be identified by reciting, “reference [1]” or simply“[1]”. A listing of the publications corresponding to each designatorcan be found at the end of the Detailed Description section.

In addition to the just described benefits, other advantages of thepresent invention will become apparent from the detailed descriptionwhich follows hereinafter when taken in conjunction with the drawingfigures which accompany it.

DESCRIPTION OF THE DRAWINGS

The specific features aspects, and advantages of the invention willbecome better understood with regard to the following descriptionappended claims, and accompanying drawings where:

FIG. 1 is a diagram depicting a general purpose computing deviceconstituting an exemplary system for implementing the invention.

FIG. 2 is a simplified flow diagram of the overall modeling processemployed by the object modeling system and method according to theinvention.

FIG. 3 is a simplified flow diagram of the overall modeling processemployed by the object modeling system and method according to theinvention in modeling a face.

FIG. 4 depicts a neutral face model that is employed in the system andmethod according to the invention.

FIG. 5 depicts an input image that is employed by the system and methodaccording to the invention to create a three dimensional face model.

FIG. 6 depicts an image wherein the facial features are detected andused to fit the input image to the face model.

FIG. 7 depicts three different views of the 3D model generated from theinput image shown in FIG. 4.

FIG. 8 depicts three different facial expression that where applied tothe face model generated by the system and method according to theinvention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In the following description of the preferred embodiments of the presentinvention, reference is made to the accompanying drawings that form apart hereof and in which is shown by way of illustration specificembodiments in which the invention may be practiced. It is understoodthat other embodiments may be utilized and structural changes may bemade without departing from the scope of the present invention.

1.0 Exemplary Operating Environment

FIG. 1 illustrates an example of a suitable computing system environment100 on which the invention may be implemented. The computing systemenvironment 100 is only one example of a suitable computing environmentand is not intended to suggest any limitation as to the scope of use orfunctionality of the invention. Neither should the computing environment100 be interpreted as having any dependency or requirement relating toany one or combination of components illustrated in the exemplaryoperating environment 100.

The invention is operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well known computing systems, environments, and/orconfigurations that may be suitable for use with the invention include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, multiprocessor systems, microprocessor-based systems,set top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, distributed computing environmentsthat include any of the above systems or devices, and the like.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Theinvention may also be practiced in distributed computing environmentswhere tasks are performed by remote processing to devices that arelinked through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

With reference to FIG. 1, an exemplary system for implementing theinvention includes a general purpose computing device in the form of acomputer 110. Components of computer 110 may include, but are notlimited to, a processing unit 120, a system memory 130, and a system bus121 that couples various system components including the system memoryto the processing unit 120. The system bus 121 may be any of severaltypes of bus structures including a memory bus or memory controller, aperipheral bus, and a local bus using any of a variety of busarchitectures. By way of example, and not limitation, such architecturesinclude Industry Standard Architecture (ISA) bus, Micro ChannelArchitecture (MCA) bus, Enhanced ISA (EISA) bus, Video ElectronicsStandards Association (VESA) local bus, and Peripheral ComponentInterconnect (PCI) bus also known as Mezzanine bus.

Computer 110 typically includes a variety of computer readable media.Computer readable media can be any available media that can be accessedby computer 110 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computer 110. Communication media typicallyembodies computer readable instructions, data structures, programmodules or other data in a modulated data signal such as a carrier waveor other transport mechanism and includes any information deliverymedia. The term “modulated data signal” means a signal that has one ormore of its characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of the any of the aboveshould also be included within the scope of computer readable media.

The system memory 130 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 131and random access memory (RAM) 132. A basic input/output system 133(BIOS), containing the basic routines that help to transfer informationbetween elements within computer 110, such as during start-up, istypically stored in ROM 131. RAM 132 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 120. By way of example, and notlimitation, FIG. 1 illustrates operating system 134, applicationprograms 135, other program modules 136, and program data 137.

The computer 110 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 1 illustrates a hard disk drive 141 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 151that reads from or writes to a removable, nonvolatile magnetic disk 152,and an optical disk drive 155 that reads from or writes to a removable,nonvolatile optical disk 156 such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 141 is typically connectedto the system bus 121 through anon-removable memory interface such asinterface 140, and magnetic disk drive 151 and optical disk drive 155are typically connected to the system bus 121 by a removable memoryinterface, such as interface 150.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 1, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 110. In FIG. 1, for example, hard disk drive 141 is illustratedas storing operating system 144, application programs 145, other programmodules 146, and program data 147. Note that these components can eitherbe the same as or different from operating system 134, applicationprograms 135, other program modules 136, and program data 137. Operatingsystem 144, application programs 145, other program modules 146, andprogram data 147 are given different numbers here to illustrate that, ata minimum, they are different copies. A user may enter commands andinformation into the computer 110 through input devices such as akeyboard 162 and pointing device 161, commonly referred to as a mouse,trackball or touch pad. Other input devices (not shown) may include amicrophone, joystick, game pad, satellite dish, scanner, or the like.These and other input devices are often connected to the processing unit120 through a user input interface 160 that is coupled to the system bus121, but may be connected by other interface and bus structures, such asa parallel port, game port or a universal serial bus (USB). A monitor191 or other type of display device is also connected to the system bus121 via an interface, such as a video interface 190. In addition to themonitor, computers may also include other peripheral output devices suchas speakers 197 and printer 196, which may be connected through anoutput peripheral interface 195. Of particular significance to thepresent invention, a camera 163 (such as a digital/electronic still orvideo camera, or film/photographic scanner) capable of capturing asequence of images 164 can also be included as an input device to thepersonal computer 110. Further, while just one camera is depicted,multiple cameras could be included as an input device to the personalcomputer 110. The images 164 from the one or more cameras are input intothe computer 110 via an appropriate camera interface 165. This interface165 is connected to the system bus 121, thereby allowing the images tobe routed to and stored in the RAM 132, or one of the other data storagedevices associated with the computer 110. However, it is noted thatimage data can be input into the computer 110 from any of theaforementioned computer-readable media as well, without requiring theuse of the camera 163.

The computer 110 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer180. The remote computer 180 may be a personal computer, a server, arouter, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above relativeto the computer 110, although only a memory storage device 181 has beenillustrated in FIG. 1. The logical connections depicted in FIG. 1include a local area network (LAN) 171 and a wide area network (WAN)173, but may also include other networks. Such networking environmentsare commonplace in offices, enterprise-wide computer networks, intranetsand the Internet.

When used in a LAN networking environment the computer 110 is connectedto the LAN 171 through a network interface or adapter 170. When used ina WAN networking environment, the computer 110 typically includes amodem 172 or other means for establishing communications over the WAN173, such as the Internet. The modem 172, which may be internal orexternal, may be connected to the system bus 121 via the user inputinterface 160, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 110, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 1 illustrates remoteapplication programs 185 as residing on memory device 181. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

The exemplary operating environment having now been discussed, theremaining parts of this description section will be devoted to adescription of the program modules embodying the invention.

2.0 A System and Method for Modeling Three Dimensional Objects fromSingle Image.

2.1 General Overview.

In the most general sense, a single image of an object is input into thesystem and method according to the invention and a three-dimensional(3D) mesh with the texture of the original image is output. An overallflow diagram of the 3D modeling system and method is shown in FIG. 2. Asshown in FIG. 2, process action 202, an image of an object is input intothe system and method according to the invention. An object detector isused to find the object to be modeled in the image, as shown in processaction 204. Then the features of the object are determined (processaction 206). If the feature determination process action shows that theobject is rotated out of the plane of the image, the amount of rotationis determined (process action 208), and this information is used torotate the model to match the input image. The features of the object tobe modeled are then used to align the feature points of the object inthe image to a generic model of the object (process action 210). Oncethe generic model is so tailored to match the object in the image, thetexture of the original image is applied (process action 212). Thetailored model can be used for various applications.

One embodiment of the invention is directed toward a face modelingsystem and method. As shown in FIG. 3, process action 302, a singleimage of a face is input. A face detector is used to find the face to bemodeled in the image, as shown in process action 304. Then the featuresof the object are determined, as shown in process action 306. If thefeature determination process action shows that the object is rotatedout of the plane of the image, the amount of rotation is determined, andthis information is used to rotate the neutral general face model tomatch the pose of the face in the input image (process action 308). Thefeatures are then used to align the feature points of the face in theimage to a generic face model (process action 310). Once the genericface model is tailored to match the face in the image, the texture ofthe face in the input image is applied to the tailored model (processaction 312). The tailored model can then be used for variousapplications, such as animation, gaming, and preparing a database forface recognition training.

2.2 Linear Class of Face Geometries

In the system and method according to the invention, the samerepresentation for the face model is used as was described in [2]. Inthis paper, a user takes two images with a small relative head motion,and two video sequences: one with head turning to each side. The userthen locates five markers in certain base images. The five markerscorrespond to the two inner eye corners, nose top, and two mouthcorners. The next processing stage then computes the face mesh geometryand the head pose with respect to the camera frame using the two baseimages and markers as input. The final stage determined the head motionsin the video sequences, and blended the images to generate a facialtexture map.

In the invention, like the above-described paper, a face is representedas a linear combination of a neutral face and some number of facemetrics. A metric is vector that linearly deforms a face in a certainway, such as to make the head wider, make the nose bigger, and so on.The face geometry is denoted by a vector S=(v_(i) ^(T), . . . , v_(n)^(T))^(T), where v_(i)=(X_(i),Y_(i),Z_(i))^(T) (i=1, . . . , n) are thevertices, and a metric by a vector M=(δv_(l), . . . , δv_(n))^(T), whereδv_(l)=(δX_(i),δY_(i),δZ_(i))^(T). Given a neutral face S^(o)=(v₁ ^(0T),. . . , v_(n) ^(0T))^(T), and a set of m metrics M^(j)=(δv₁ ^(jT), . . ., δv_(n) ^(jT))^(T), the linear space of face geometries spanned bythese metrics is $\begin{matrix}{S = {{S^{0} + {\sum\limits_{j = 1}^{m}{c_{j}M^{j}\quad{subject}\quad{to}\quad c_{j}}}} \in \left\lbrack {l_{j},u_{j}} \right\rbrack}} & (1)\end{matrix}$where the S is the new face, the c_(j)'s are the metric coefficients(e.g., scalars to scale the metrics) which will be different fordifferent faces and l_(j) and u_(j) are the valid range of c_(j).

The neutral face model and all of the associated metrics are typicallydesigned by an artist. It is only necessary to create the neutral facerepresentation once. In one embodiment of the invention, the neutralface representation (as shown in FIG. 4) contains 194 vertices 402 and360 triangles 404. There are 65 metrics in this embodiment. It should benoted, however, that the neutral face could also be represented withless or more vertices and triangles.

2.3 Face Model from a Single View.

2.3.1 Face Detection and Feature Alignment.

Given an image of a face, to find the feature points on the face, onefirst uses face detection software to detect the face. Any conventionalface detection software can be used for this purpose. Once the face isfound, the facial features are located on the face. Locating thesefeatures can also be performed in a variety of conventional ways. In oneembodiment of the invention, face feature alignment software by Yan etal. was used to find the face features. The method is described in [3].In this embodiment of the invention, the features used were theeyebrows, eyes, nose, mouth and sides of the face. These features can befound by various conventional techniques such as by using patternrecognition, edge detection, or a generic head model. The features inthe image and the features in the model are used to align the genericmodel to the image. If all features or feature points are not visible,additional feature points can be estimated by interpolation between theexisting known features.

FIG. 5 shows an input image and FIG. 6 shows the feature alignmentresult. In the embodiment of the invention, shown in FIG. 6, thefeatures selected were the eyebrows, nose, edges of the face and theeyes. However, other features could also be used for the purposes ofcalculating alignment of the input image and the model.

2.3.2 Model Fitting

It is assumed that the projection of v_(i) onto the plane is orthogonal,and there are no out-of-the plane rotations for the face. Each of thevertices of the generic face model is described in a 3D coordinatesystem. In order to adjust the generic model to the object to bemodeled, the differences from the neutral face model are calculated(e.g., the 65 metrics). Without loss of generality, denotev_(i)=(X_(i),Y_(i),Z_(i))^(T), (i=1, . . . , f) to be the featurepoints. Denote v_(i) =(X_(i),Y_(i)) to be the projection of v_(i) on theXY plane. For each feature point v_(i), denote m_(i) to be itscorresponding coordinate on the input image. Let R denote the 2×2rotation matrix, be the 2D translation vector, and s be the scale. Thenthe following equation results:sR v_(i) = t=m _(i)  (2)From equation 1, $\begin{matrix}{{\overset{\_}{v_{i}} = {\overset{\_}{v_{i}^{0}} + {\sum\limits_{j = 1}^{m}{c_{j}\delta\quad\overset{\_}{v_{i}^{j}}}}}}{{Therefore},}} & (3) \\{{{{sR}\left( {\overset{\_}{v_{i}^{0}} + {\sum\limits_{j = 1}^{m}{c_{j}\delta\quad\overset{\_}{v_{i}^{j}}}}} \right)} + t} = m_{i}} & (4)\end{matrix}$This equation is solved iteratively. In one embodiment of the invention,a conventional technique described by B. K. Horn [4] for determining aclosed-form solution to the least-squares problem for three or morepoints is employed. First one fixes c_(j) and solves for s, R and t.Then s, R and t are fixed, and equation 4 becomes a linear system whichcan be solved by using a linear least square procedure. One can thenre-estimate s, R and t by using the new estimates of c_(j)'s, and so on.In experiments, for one exemplary embodiment of the invention, it wasfound that one or two iterations are usually sufficient.

Suppose c₁,c₂, . . . ,c_(m) are the solutions of equation 4. Thenaccording to equation 1, the mesh of the face is$S = {S^{0} + {\sum\limits_{j = 1}^{m}{c_{j}{M^{j}.}}}}$

2.3.3 Applying Texture to the Model

Once the generic face model is tailored to match the face in the image,the texture of the original image can be applied to the tailored genericface model. For each vertex v=(X,Y,Z)^(T) on the face mesh, itscoordinate on the image is m=sR v+t, where s,R,t are the solutions ofequation 4. Assuming m=(m_(x),m_(y)), its texture coordinate is set tobe $\left( {\frac{m_{x}}{width},\frac{m_{y}}{height}} \right)$where width and height are the width and height of the input image,respectively. Given a mesh and a texture image, there are well-knownprocedures that use the existing graphics library such as MicrosoftCorporation's DirectX or OpenGL to render the mesh with the texture.

FIG. 7 shows the different views of the reconstructed 3D model based onthe input image in FIG. 3. One can see the frontal view (the image inthe middle) looks very good as expected. There are quite large rotationsfor the images on the left and right. These two images still look quiterecognizable. The images used in this exemplary working embodiment ofthe invention are 640×480 pixels. The total computation time for eachimage was about 7 seconds on a 1.7 GHz PC. The main computation cost wasthe face alignment program.

Once the face in the image has been modeled it can be used for variousapplications. It can be used to construct personalized models for games,online chat, and so on. It can also be used as a tool to generate adatabase of faces

1. A computer-implemented process for generating a three dimensional model of an object from a single image, comprising the process actions of: obtaining an image of an object; identifying at least one object to be modeled in the image; determining features of the object in the image; determining if the object is rotated in the image; and aligning the features of a generic model of the identified object with the features of the object in the image, using any rotation, to obtain a tailored model.
 2. The computer-implemented process of claim 1 further comprising the process action of: applying texture of the object in the image to the tailored generic model.
 3. The computer-implemented process of claim 1, wherein determining if the object is rotated in the image comprises the process actions of: determining the amount of out of plane rotation of the object in the image; and using the amount of rotation of the object in the image to rotate the generic model to match the input image.
 4. The computer-implemented process of claim 1 wherein the process action of determining the features of the object comprises determining the features by at least one of the following: using a pattern recognition technique to locate the features; an edge detection technique to locate the features; and using a generic model of the object to locate the features.
 5. The computer-implemented process of claim 1 wherein the generic model of the object is represented as a linear combination of a neutral object, and some number of object metrics, wherein an object metric is a vector that linearly deforms the object in a certain way.
 6. The computer-implemented process of claim 4 wherein the linear combination of a neutral object is represented by a mesh of vertices and triangles that represent the shape of the object.
 7. The computer-implemented process of claim 4 wherein the process action of aligning the feature of a generic model of the object to the features of the object in the image to obtain a tailored model comprises modifying the object metrics so that the generic model matches the shape of the object in the image.
 8. The computer-implemented process of claim 1 wherein the object modeled is a face.
 9. The computer-implemented process of claim 1 wherein the object modeled is a human body.
 10. The computer-implemented process of claim 1 wherein multiple objects are modeled by repeating the following process actions for more than one object: identifying at least one object to be modeled in the image; determining features of the object in the image; and aligning the features of a generic model of the identified object with the features of the object in the image to obtain a tailored model.
 11. A system for creating a face model from the input of a single image of a face, the system comprising: a general purpose computing device; and a computer program comprising program modules executable by the computing device, wherein the computing device is directed by the program modules of the computer program to, input a single image of a face; use a face detector to find the face to be modeled in the image; determine features of the face to be modeled; align features of a generic face model to features of the face in the image to obtain a tailored face model; and apply the texture of the face in the image to the tailored generic face model.
 12. The system of claim 11 further comprising modules to: determine if the face in the image is rotated out of the plane of the image prior to using the features of the face in the image to align the features of a generic face model with the features of the face; determine the amount of out of plane rotation of the face in the image; and use the amount of rotation of the face in the image to rotate the generic face model to match the face in the input image.
 13. The system of claim 11 wherein the features of the face to be modeled comprise two eyebrows, two eyes, a nose, a mouth and two sides of a face.
 14. The system of claim 11 wherein the generic model of a face is represented as a linear combination of a neutral faces and some number of face metrics, wherein a face metric is a vector that can linearly deform the neutral face model in a certain way.
 15. The system of claim 14 wherein the linear combination of a neutral face is represented by a mesh of vertices and triangles that represent the shape of the face.
 16. The system of claim 14 wherein using the features of the object to be modeled to align the features of a generic face model to the feature points of the face in the image comprises modifying the face metrics so that the generic face model matches the shape of the face in the image.
 17. The system of claim 14 wherein using the features of the face to be modeled to align the features of a generic face model to the features of the face in the image comprises modifying the face metrics so that the generic face model matches the shape of the face in the image.
 18. A computer-readable medium having computer-executable instructions for creating a three-dimensional model of an object in a single image, said computer executable instructions comprising: inputting a single image of a three-dimensional object; creating a generic linear space representation of the three-dimensional object depicted in the image; estimating object pose and linear coefficients describing the three-dimensional object in the image in the linear space using an orthogonal projection; using the estimated pose and linear coefficients to obtain a linear space model of the object in the image; and applying texture information of the three-dimensional object in the image to the linear space model of the object.
 19. The computer-readable medium of claim 18 wherein said computer-executable instruction for creating a generic linear space representation of the three-dimensional object depicted in the image, comprises sub-instructions for: using an object detector to find the object to be modeled in the image; determining features of the object; and using the features of the object to be modeled to align the features of a generic linear space representation of the object to the shape of the object in the image.
 20. The computer-readable medium of claim 18 further comprising a computer-executable instruction for changing the characteristics of the linear space model of the object by changing the shape of the linear space model of the object in the image underlying the texture. 